Environment Management

To package a Python application along with its dependencies into a single executable, use PyInstaller.

With PyInstaller, users can run the packaged app without installing a Python interpreter or any modules.

It’s a good practice to store sensitive information in a .env file and load it into your Python script using python-dotenv.

This helps protect the information from being exposed in your codebase or version control systems.

pip freeze saves all packages in the environment, including ones that you don’t use in your current project.

To generate a requirements.txt based on imports, use pipreqs.

When passing data between different web requests, there is a risk of malicious code injection.

To ensure the safety of passing data to untrusted environments, use ItsDangerous. ItsDangerous adds a unique signature to the data to verify that the data has not been tampered with during transmission.

Have you ever updated a dependency of your project to a new version, and your code suddenly broke? That could be due to the incompatibility of the current dependencies and the new dependency.

Wouldn’t it be nice if you can check the compatibility between dependencies before installing new ones? That is when Poetry comes in handy.

In the code above, since pandas==1.4.1 requires numpy>=1.18.5, numpy<1.18 is not installed.

Another cool thing about Poetry is that when you remove a dependency, it also removes sub-dependencies that are no longer needed in your project.

Link to Poetry.

My article about Poetry.
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It can be cumbersome to compare the dependencies between two requirements files. Especially when there are a lot of dependencies in each file. To automate the comparison, use compare-requirements.

The code above shows the comparison of 2 requirements files using compare-requirements.

Link to compare-requirements.

My previous tips on tools to manage your environment.
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When using pip uninstall, you only remove a specific package. Wouldn’t it be nice if you can install that package and its unused dependencies?

That is when pip-autoremove comes in handy.

In the code above, using pip-autoremove removes pandas-profiling and its unused dependencies.

Link to pip-autoremove.

Link to previous tips on some useful tools I found.
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